Library loading

import scanpy as sc, anndata as ad, numpy as np, pandas as pd, seaborn as sns
from scipy import sparse
from anndata import AnnData
from matplotlib import pylab
import warnings
import scanpy.external as sce
import socket
import matplotlib.pyplot as plt

import yaml
import os
import scvelo as scv

warnings.filterwarnings('ignore')

sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
with open("../data/resources/rcParams.yaml") as f:
    rcParamsDict = yaml.full_load(f)
    for k in rcParamsDict["rcParams"]:
        print("{} {}".format(k,rcParamsDict["rcParams"][k]))
        plt.rcParams[k] = rcParamsDict["rcParams"][k]
    for k1 in set(list(rcParamsDict)).difference(set(["rcParams"])):
        print("{} {}".format(k1,rcParamsDict[k1]))

scanpy==1.8.0 anndata==0.8.0 umap==0.4.6 numpy==1.22.2 scipy==1.6.2 pandas==1.2.3 scikit-learn==0.24.1 statsmodels==0.13.5 python-igraph==0.9.1 louvain==0.7.0 leidenalg==0.8.3
figure.dpi 80
savefig.dpi 500
figure.figsize [10, 10]
axes.facecolor None
figure.facecolor None
dotSize 20

plt.rcParams["savefig.dpi"] = 300

Configure paths

outdir = "../data/output"

with open("../data/resources/iPSC_lines_map.yaml", 'r') as f:
    iPSC_lines_map = yaml.load(f, Loader=yaml.FullLoader)["lines"]
colorsmap = dict(zip([i["newName"] for i in iPSC_lines_map.values()],[i["color"] for i in iPSC_lines_map.values()]))

Import anndata_all

adata = sc.read_h5ad(outdir+"/adatas/ClusterAnnotated_Base_filt.h5ad")

Markers plot

mPlotList = ["CDC20","MKI67","TOP2A","VIM","SOX2","NES","AQP4",
            "S100B","HOPX","CBLN1","RELN","PAX6","EOMES","TAGLN3","FOXP1",
            "CA8","LHX1","SLC17A6","NOVA1","SATB2","SLC17A7","GRIA2", "DCX", 
             "BCL11B", "TUBB3", "MAP2", "NEUROD1",
            "GAD2","DLX5","DLX6-AS1","EOMES"]

import matplotlib

sc.settings._vector_friendly = True

Split=5
for mlist in enumerate([mPlotList[(len(mPlotList)//Split)*i : (len(mPlotList)//Split)*(i+1) ] for i in list(range(Split))]+[mPlotList[-(len(mPlotList)%Split):]]):
    sc.pl.draw_graph(adata, color=mlist[1], size = 50, add_outline = True,outline_width=(0.2, 0.05),vmin="p1",vmax="p99", frameon=False,  save="markers."+str(mlist[0])+".svg")

WARNING: saving figure to file figures/draw_graph_famarkers.0.svg

WARNING: saving figure to file figures/draw_graph_famarkers.1.svg

WARNING: saving figure to file figures/draw_graph_famarkers.2.svg

WARNING: saving figure to file figures/draw_graph_famarkers.3.svg

WARNING: saving figure to file figures/draw_graph_famarkers.4.svg

WARNING: saving figure to file figures/draw_graph_famarkers.5.svg

sc.pl.draw_graph(adata, color="cellID_newName", size = 10, add_outline = True,outline_width=(0.2, 0.05),vmin="p1",vmax="p99", frameon=False, save = ".genotype.svg")

WARNING: saving figure to file figures/draw_graph_fa.genotype.svg

sc.pl.draw_graph(adata, color="stage", size = 7, add_outline = True,outline_width=(0.1, 0.05),vmin="p1",vmax="p99", frameon=False,  save= ".stage.svg")

WARNING: saving figure to file figures/draw_graph_fa.stage.svg

sc.pl.draw_graph(adata, color="type", size = 7, add_outline = True,outline_width=(0.1, 0.05),vmin="p1",vmax="p99", frameon=False,  save="type.svg")

WARNING: saving figure to file figures/draw_graph_fatype.svg

with open("../data/resources/rcParams.yaml") as f:
    rcParamsDict = yaml.full_load(f)
    for k in rcParamsDict["rcParams"]:
        print("{} {}".format(k,rcParamsDict["rcParams"][k]))
        plt.rcParams[k] = rcParamsDict["rcParams"][k]
    for k1 in set(list(rcParamsDict)).difference(set(["rcParams"])):
        print("{} {}".format(k1,rcParamsDict[k1]))
sc.pl.draw_graph(adata, color="leidenAnnotated", size = 50,add_outline = True,outline_width=(0.1, 0.05),vmin="p1",vmax="p99", frameon=False,  save="leiden.png")

figure.dpi 80
savefig.dpi 500
figure.figsize [10, 10]
axes.facecolor None
figure.facecolor None
dotSize 20
WARNING: saving figure to file figures/draw_graph_faleiden.png

Plot markers dotplot

pylab.rcParams['figure.figsize']

[10.0, 10.0]

LeidenOrder = ['ProliferatingProgenitors', 
               'RadialGliaProgenitors',
               'OuterRadialGliaAstrocytes',
               'CajalR_like',
               'intermediateProgenitors',
                'Neurons', 
               'MigratingNeurons',
               'GlutamatergicNeurons_early',
               'GlutamatergicNeurons_late', 
               'Interneurons_GAD2',
               'Interneurons']

adata.raw.to_adata().X.data

array([0.57579654, 0.57579654, 0.57579654, ..., 2.5117002 , 1.3814054 ,
       1.7524617 ], dtype=float32)

sc.pl.dotplot(adata, mPlotList, 'leidenAnnotated', figsize = [10,5], categories_order = LeidenOrder, cmap="bwr", standard_scale="var",  save="dotplotMarkers.svg", colorbar_title = "Expression \nin group (log)")

WARNING: saving figure to file figures/dotplot_dotplotMarkers.svg

with open("../data/resources/rcParams.yaml") as f:
    rcParamsDict = yaml.full_load(f)
    for k in rcParamsDict["rcParams"]:
        print("{} {}".format(k,rcParamsDict["rcParams"][k]))
        plt.rcParams[k] = rcParamsDict["rcParams"][k]
    for k1 in set(list(rcParamsDict)).difference(set(["rcParams"])):
        print("{} {}".format(k1,rcParamsDict[k1]))

figure.dpi 80
savefig.dpi 500
figure.figsize [10, 10]
axes.facecolor None
figure.facecolor None
dotSize 20

import random
groupedOBS = adata.obs.groupby(["type","stage"],as_index=False).size()
SamplingBCs = []
for tpt in groupedOBS.stage.unique():
    adataSlice = adata[adata.obs["stage"] == tpt]
    minCells = groupedOBS.loc[groupedOBS.stage == tpt,"size"].min()

    SampledCells = [random.sample(adataSlice[adataSlice.obs["type"] == i].obs_names.tolist(),minCells)  for i in adataSlice.obs["type"].unique()]
    SamplingBCs.extend([item for sublist in SampledCells for item in sublist])
    
adataSlice = adata[SamplingBCs]

pd.crosstab(adataSlice.obs["stage"],adataSlice.obs["type"])

type	upstream	downstream
stage
early	2034	2034
mid	1522	1522
late	1771	1771

sc.pl.draw_graph(adataSlice[adataSlice.obs["type"] == "upstream"], color="cellID_newName", size = 10, title="Mosaic Genotypes",
               add_outline = True,outline_width=(0.2, 0.05),vmin="p1",vmax="p99", frameon=False, ncols=2)

sc.pl.draw_graph(adataSlice[adataSlice.obs["type"] == "downstream"], color="cellID_newName", size = 10, title="Downstream Genotypes",
               add_outline = True,outline_width=(0.2, 0.05),vmin="p1",vmax="p99", frameon=False, ncols=2)

AllAggregatedgrouped = adata.obs[["dataset","cellID_newName"]].groupby(["dataset","cellID_newName"], as_index=False).size()
AllAggregatedgrouped.loc[:,"paradigm"] = "upstream"
AllAggregatedgrouped.loc[AllAggregatedgrouped["dataset"].isin(["D100Down","D250Down","D50Down"]),"paradigm"] = "downstream"
AllAggregatedgrouped.loc[:,"stage"] = "mid"
AllAggregatedgrouped.loc[AllAggregatedgrouped["dataset"].isin(["D50Down","D50Up"]),"stage"] = "early"
AllAggregatedgrouped.loc[AllAggregatedgrouped["dataset"].isin(["D250Down","D300Up"]),"stage"] = "late"
AllAggregatedgrouped.rename(columns={"Consensus":"cellID_newName","size":"nCells"}, inplace=True)
AllAggregatedgrouped.head()


for ds in list(AllAggregatedgrouped.dataset.unique()):
    sns.set_style("whitegrid")

    localDF = AllAggregatedgrouped[AllAggregatedgrouped["dataset"] == ds]
    localDF["cellID_newName"] = localDF["cellID_newName"].astype("category")
    localDF["cellID_newName"] = localDF["cellID_newName"].cat.set_categories([iPSC_lines_map[k]["newName"] for k in list(iPSC_lines_map.keys()) if iPSC_lines_map[k]["newName"] in localDF["cellID_newName"].tolist()], ordered = True)

    plt.figure(figsize=(10, 6))
    fig = sns.barplot(x="cellID_newName", y="nCells",linewidth=1.5,edgecolor=".2", data=localDF, order=adata.obs.cellID_newName.cat.categories.tolist(),
                      palette=colorsmap).set(title=ds+" aggregatedCall")
    sns.despine(left=True, bottom=True)

    fig.clear